srivarshan123 / -implementation-of-logistic-regression-using-gradient-descent

Implementation-of-Logistic-Regression-Using-Gradient-Descent

AIM:

To write a program to implement the the Logistic Regression Using Gradient Descent.

Equipments Required:

1. Hardware – PCs
2. Anaconda – Python 3.7 Installation / Moodle-Code Runner

Algorithm

1. Start the program.
2. Import libraries.
3. Read the dataset of social networks ads.
4. from sklearn import train_test_split.
5. Preprocessing import standardscaler.
6. sklearn import logisticregression.
7. Use a classifier for fit(x_train,y_train)
8. From sklearn import confusion matrix,accuracy.
9. Recall sensitivity metrics score(y_test,y_pred)
10. Import listed colomap and plot it.
11. Plot scatter and plot title,Age,estimated salary.
12. Stop the program.

Program:

/*
Program to implement the the Logistic Regression Using Gradient Descent.
Developed by: Srivarshan.S
RegisterNumber:  212221040163
*/
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset=pd.read_csv('/content/sample_data/Social_Network_Ads (1).csv')
X=dataset.iloc[:, [2,3]].values
Y=dataset.iloc[:, 4].values
from sklearn.model_selection import train_test_split
X_Train,X_Test,Y_Train,Y_Test=train_test_split(X,Y,test_size=0.25,random_state=0)
from sklearn.preprocessing import StandardScaler
sc_X=StandardScaler()
sc_X
X_Train=sc_X.fit_transform(X_Train)
X_Test=sc_X.transform(X_Test)
from sklearn.linear_model import LogisticRegression
classifier=LogisticRegression(random_state = 0)
classifier.fit(X_Train, Y_Train)
Y_Pred=classifier.predict(X_Test)
Y_Pred
from sklearn.metrics import confusion_matrix
cm=confusion_matrix(Y_Test, Y_Pred)
cm
from sklearn import metrics
accuracy=metrics.accuracy_score(Y_Test, Y_Pred)
accuracy
recall_sensitivity=metrics.recall_score(Y_Test,Y_Pred,pos_label=1)
recall_specificity=metrics.recall_score(Y_Test,Y_Pred,pos_label=0)
recall_sensitivity, recall_specificity
from matplotlib.colors import ListedColormap
X_set,Y_set=X_Train,Y_Train
X1,X2=np.meshgrid(np.arange(start=X_set[:,0].min()-1,stop=X_set[:,0].max()+1,step=0.
plt.contourf(X1,X2,classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X
plt.xlim(X1.min(),X2.max())
plt.ylim(X2.min(),X2.max())
for i,j in enumerate(np.unique(Y_set)):
plt.scatter(X_set[Y_set==j,0],X_set[Y_set==j,1],c=ListedColormap(('black','yellow'))
(i),label=j)
plt.title('Logistic Regression(Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()

Output:

Result:

Thus the program to implement the the Logistic Regression Using Gradient Descent is written and verified using python programming.